Abstract

This study examines a dataset of COVID-19-related tweets collected during the pandemic to understand public sentiment and emotional responses. The database consists of categorized tweets, classified into sentiment groups such as extremely positive, positive, neutral, negative, and extremely negative. Methodologically, the data was pre-processed and analyzed using statistical techniques and visualization tools to identify sentiment patterns. The results reveal that the majority of tweets reflected neutral and moderately negative opinions, with fewer tweets showing extreme sentiments. Visualization through bar charts and pie charts provided a clear representation of sentiment distribution, making the findings more accessible and interpretable. The study highlights the importance of monitoring social media platforms to gain real-time insights into public perception during health crises.

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